{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 逻辑回归预测芯片质量通过"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**任务:**\n",
    "\n",
    "1、基于chip_test.csv数据，建立逻辑回归模型(二阶边界)，评估模型表现；\n",
    "2、以函数的方式求解边界曲线\n",
    "3、描绘出完整的决策边界曲线"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@Author  : Flare Zhao\n",
    "@QQ-Email & Wechat: 454209979\n",
    "@QQ讨论群：530533630  申请加群的验证信息为订单号（粘贴号码数字即可）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data; visualize data;#generate new data\n",
    "#establish model and train it; predict\n",
    "#accuracy\n",
    "#decision boundary\n",
    "#define f(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test1</th>\n",
       "      <th>test2</th>\n",
       "      <th>pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.93348</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      test1    test2  pass\n",
       "0  0.051267  0.69956     1\n",
       "1 -0.092742  0.68494     1\n",
       "2 -0.213710  0.69225     1\n",
       "3 -0.375000  0.50219     1\n",
       "4  0.183760  0.93348     0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('chip_test.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      False\n",
      "1      False\n",
      "2      False\n",
      "3      False\n",
      "4       True\n",
      "       ...  \n",
      "113     True\n",
      "114     True\n",
      "115     True\n",
      "116     True\n",
      "117     True\n",
      "Name: pass, Length: 118, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#add label mask\n",
    "mask=data.loc[:,'pass']==1\n",
    "print(~mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the data\n",
    "%matplotlib inline \n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure()\n",
    "passed=plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed=plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           X1        X2      X1_2      X2_2     X1_X2\n",
      "0    0.051267  0.699560  0.002628  0.489384  0.035864\n",
      "1   -0.092742  0.684940  0.008601  0.469143 -0.063523\n",
      "2   -0.213710  0.692250  0.045672  0.479210 -0.147941\n",
      "3   -0.375000  0.502190  0.140625  0.252195 -0.188321\n",
      "4    0.183760  0.933480  0.033768  0.871385  0.171536\n",
      "..        ...       ...       ...       ...       ...\n",
      "113 -0.720620  0.538740  0.519293  0.290241 -0.388227\n",
      "114 -0.593890  0.494880  0.352705  0.244906 -0.293904\n",
      "115 -0.484450  0.999270  0.234692  0.998541 -0.484096\n",
      "116 -0.006336  0.999270  0.000040  0.998541 -0.006332\n",
      "117  0.632650 -0.030612  0.400246  0.000937 -0.019367\n",
      "\n",
      "[118 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#define X,y\n",
    "X = data.drop(['pass'],axis=1)\n",
    "y = data.loc[:,'pass']\n",
    "X1 = data.loc[:,'test1']\n",
    "X2 = data.loc[:,'test2']\n",
    "X1.head()\n",
    "#create new data\n",
    "X1_2 = X1*X1\n",
    "X2_2 = X2*X2\n",
    "X1_X2 = X1*X2\n",
    "X_new = {'X1':X1,'X2':X2,'X1_2':X1_2,'X2_2':X2_2,'X1_X2':X1_X2}\n",
    "X_new = pd.DataFrame(X_new)\n",
    "print(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#establish new model and train\n",
    "#establish the model and train it\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR2 = LogisticRegression()\n",
    "LR2.fit(X_new,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8135593220338984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y2_predict = LR2.predict(X_new)\n",
    "accuracy2 = accuracy_score(y,y2_predict)\n",
    "print(accuracy2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\env_2305_tf_new\\lib\\site-packages\\pandas\\core\\arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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PiEYI9fdCdmFptROu+pylHhGNbPL4ly5dwvTp0/Hss8/i2LFj+Pjjj/H++++jWbNm8PDwwMcff4znnnsOp06dwsKFC43uO2/ePHTt2hV33303ysrK8MMPP6Bdu3YAgA8++AChoaHo3Lkz5HI5vv32W6hUKgQEBCAuLg6xsbF4+OGH8e677+Kuu+7CtWvX8L///Q+PPPIIunXrhpdeegnjx49Ht27dcM8992Djxo04ffo0WrZsaZPn4XYMlojIduQKJnFbkz4fTJ2F6vOWZLrbmQ8maQq5DPOHRGHKhmOQwfiV1k9szx8SZfXkbr2xY8fi1q1b6NGjBxQKBV566SVMnjwZMpkM69atw7///W989NFH6NKlC5YuXYqhQ/8ZKfbw8MCcOXNw4cIFeHt7o0+fPti0aRMAwM/PD++++y7Onj0LhUKB7t2748cff4Rcrpvk+vHHH/Haa69hwoQJuH79OlQqFe69916EhOiC/1GjRiEjIwMzZ85EaWkpRowYgSlTpuCnn36yyfNwO66GswKuhiMiuzGshgOqfRvlNKfD1Xc1nF7CqSws2JFqlOwd6u+F+UOiEN8+1MQ9665fv36Ijo7GsmXLbHJ+R7DGajiOLBERSYk+H6zaffcWM1ByIvHtQ3F/lApJmfnIvVmKYD/d1JutRpSoZgyWiIikhvlgLkMhlyG2VWNHd8PlMVgiIpIi5oORDezfv9/RXRAllg4gIiIiMoHBEhEREZEJDJaIiIiITGCwRERERGSCpIKlX3/9FUOGDEFYWBhkMhm2bt1a633279+PLl26wNPTE5GRkVi3bl2VNsuXL0eLFi3g5eWFmJgYJCUlWb/z5BhaDZD5G3Bys+47NxiVHr6GRORgkloNV1xcjE6dOuHpp5/G8OHDa22fmZmJwYMH47nnnsPGjRuxZ88ePPPMMwgNDcWgQYMAAF9//TWmT5+OlStXIiYmBsuWLcOgQYOQnp6O4OBgW18S2VLq9hpq0SxhLRpr02pss4ydryERiYCkRpYeeOABvPXWW3jkkUfMar9y5UpERETg/fffR7t27TB16lQ8+uij+M9//mNo88EHH2DSpEmYMGECoqKisHLlSvj4+GDNmjW2ugyyB32V4zt3aFdn6Y6nbndMv5xR6nZgWXtg/UPAdxN135e1r/9zzNeQyO4EQcDkyZPRqFEjyGQypKSkmGx/4cIFo3b79++HTCZDQUFBvfrRokULUVURl1SwZKnExETExcUZHRs0aBASExMBAOXl5UhOTjZqI5fLERcXZ2hTnbKyMqjVaqMvEhGtRjcaUe3eWX8fS5jN6RxrsFVAw9eQyCESEhKwbt06/PDDD8jKykL79u1Ntg8PDzerndQ5dbCUnZ1t2IBPLyQkBGq1Grdu3cKNGzeg0WiqbZOdnV3jeRctWgR/f3/DV3h4uE36T3V08VDVN28jAqC+qmtHdWfLgIavIZGOnXP2MjIyEBoail69ekGlUsHNzXS2jkKhMKud1Dl1sGQrc+bMQWFhoeHr8uXLju4S3a4ox7rtqHq2DGj4GhLZboq7BuPHj8cLL7yAS5cuQSaToUWLFkhISEDv3r0REBCAxo0b46GHHkJGRobhPndOw1XnwIED6NOnD7y9vREeHo4XX3wRxcXFhttzc3MxZMgQeHt7IyIiAhs3brTJ9dWHUwdLKpUKOTnGf0xzcnKgVCrh7e2NwMBAKBSKatuoVKoaz+vp6QmlUmn0RSLiG1J7G0vaiYhGKyAxIw/bUq4iMSMPGm11ozp2YsuAxolfQyKzOCBn78MPP8Sbb76Jpk2bIisrC0eOHEFxcTGmT5+Oo0ePYs+ePZDL5XjkkUeg1WrNOmdGRgbi4+MxYsQInDhxAl9//TUOHDiAqVOnGtqMHz8ely9fxr59+7B582Z8+umnyM3Ntfr11YdTj5vFxsbixx9/NDq2a9cuxMbGAgA8PDzQtWtX7NmzBw8//DAAQKvVYs+ePUYvJElM8166FVPqLFQ/RSTT3d68l717Vi8Jp7KwYEcqsgpLDcdC/b0wf0gU4tuH2r9DtgxonPQ1JDJLrVPcMt0Ud9vBVt082d/fH35+foapNQAYMWKEUZs1a9YgKCgIqampZuUpLVq0CE8++SSmTZsGAGjdujU++ugj9O3bFytWrMClS5ewc+dOJCUloXv37gCA1atXo127dla7LmuQ1MhSUVERUlJSDMN9mZmZSElJwaVLlwDopsfGjh1raP/cc8/h/PnzmDlzJs6cOYNPP/0U33zzDV5++WVDm+nTp2PVqlVYv3490tLSMGXKFBQXF2PChAl2vTayIrlCt7QcACC748a/f45fLKkd2hNOZWHKhmNGgRIAZBeWYsqGY0g4lWX/TukDmirPsZ4MUDapW0DjhK8hkdlElLN39uxZPP7442jZsiWUSiVatGgBAIb33dr88ccfWLduHXx9fQ1fgwYNglarRWZmJtLS0uDm5oauXbsa7tO2bVsEBATY4GrqTlLB0tGjR9G5c2d07twZgC7Q6dy5M+bNmwcAyMrKMnoBIyIi8L///Q+7du1Cp06d8P777+P//u//DDWWAGDUqFFYunQp5s2bh+joaKSkpCAhIaFK0jdJTNRQYOQXgPKOERdlmO64hGr0aLQCFuxINZVGjQU7Uu0/JWfrgMaJXkMii4goZ2/IkCHIz8/HqlWrcPjwYRw+fBiAbjW5OYqKivDss88aBjpSUlLwxx9/4OzZs2jVqpUtu25VkpqG69evHwSh5jeE6qpz9+vXD8ePHzd53qlTp3LazRlFDdUNU9uiWKIdJWXmVxlRup0AIKuwFEmZ+Yht1dh+HQP+CWiqLRy5uP4BjZO8hkQWEUnOXl5eHtLT07Fq1Sr06dMHgC5Z2xJdunRBamoqIiMjq729bdu2qKysRHJysmEaLj09vd51mqxNUsESkcXkCiCij6N7US+5N2sOlOrSzupsHdBI9TW0VVVzcn4iydlr2LAhGjdujM8//xyhoaG4dOkSZs+ebdE5Zs2ahZ49e2Lq1Kl45pln0KBBA6SmpmLXrl345JNP0KZNG8THx+PZZ5/FihUr4ObmhmnTpsHb29tGV1U3kpqGI3JFwX5eVm1nE/qApsOjuu+uHhTYeck3ORmR5OzJ5XJs2rQJycnJaN++PV5++WW89957Fp2jY8eO+OWXX/Dnn3+iT58+htSZsLAwQ5u1a9ciLCwMffv2xfDhwzF58mTRbTcmE0zNa5FZ1Go1/P39UVhYyDICZHUarYDeS/Yiu7C0ps+YUPl74cCs/lDIa0q2JrvRL/mu8mr9/dow38rplZaWIjMzExEREfDyqseHmGr3RmxinSluF2Lq9TD3/ZvTcET1ZePpFoVchvlDojBlwzHIYPwWrA+N5g+JYqBUHXtPhTloyTc5KebsiQaDJaL6qPaTX5huCN2Kn/zi24dixZguVeosqRxZZ0ns7PTaGLFkybcU87DI/qSas+dkGCwR1VVN0y36CrtWnm6Jbx+K+6NUSMrMR+7NUgT7eaFHRCOOKFXHzq+NgYiWfBOR9TBYIudhzykXB023KOQy+5cHkBpHToWJZMk3EVkXgyVyDvaecuF0i3g58rURyZJvEgeunxIHa7wOLB1A0ueADSc53SJijnxtRLLkmyyk1QCZvwEnN+u+azX1Op27uzsAoKSkxBq9o3rSvw7616UuOLJE0uaoKRdOt4iXo18bW1c1J+uywai0QqFAQEAAcnNzAQA+Pj6QyZhbaG+CIKCkpAS5ubkICAiAQlH39wAGSyRtjppy4XSLeInhteGSb2mw4UIAlUoFAIaAiRwnICDA8HrUFYMlkjZHTbnop1u+GQvUVP2I0y2OIZbXhku+xc3Go9IymQyhoaEIDg5GRUVFfXtLdeTu7l6vESU9BkskbY6ccuF0i3jxtaHa2GlUWqFQWOXNmhyLwRJJm6OnXDjdIl58bcgULtIgCzBYImkTw5QLp1vEi68N1cTRCwFIUlg6gKRPP+WivGPLD2UYNy0lourpR6WrlHjQk+k2reUiDQJHlshZcMqFiCwhhlFpkgwGS+Q8OOVCRJbgQgAyE4MlIiJyXRyVJjMwWCIiItfGUWmqBYMlIiKqmVbDURdyeQyWiMhhNFoBSZn5yL1ZimA/L/SIaASFnHtoiYYN9k0jkiIGS0TkEAmnsrBgRyqyCksNx0L9vTB/SBTi24eauCfZhQ33TSOSGtZZIiK7SziVhSkbjhkFSgCQXViKKRuOIeFUloN6RgDM2DcNun3TtBp79orIYRgsEZERjVZAYkYetqVcRWJGHjTa6t4w63f+BTtSTb0NY8GOVKs/LlnAkn3TiFwAp+GIyMAeU2NJmflVRpRuJwDIKixFUmY+Yls1tspjkoW4bxqREY4sEREA+02N5d6sOVCqSzuyAe6bRmSEwRIR2XVqLNjPy6rtyAa4bxqREQZLRCJj65yh6lgyNVZfPSIaIdTfy9TbMEL9dWUE7EqrATJ/A05u1n23R/KyIx7THPp90wBUDZi4bxq5HuYsEYmIo5bT23NqTCGXYf6QKEzZcKym7Usxf0iUfestOaKekNhrGHHfNCIDmSAIXHJST2q1Gv7+/igsLIRSqXR0d0ii9DlDd/5C6kOGFWO62CxgSszIw+Orfq+13VeTelot6Vo0dZZqqiekf+ZtUU/IEY9ZV6zgTU7M3PdvBktWwGCJ6kujFdB7yd4ap8JkAFT+Xjgwq79NRlz0j59dWFpt3pKtHt/hFby1GmBZexPL5GW6kZRpJ60XIDjiMYmoWua+fzNniUgE7JkzVB391BhQY4aKTabGFHIZYls1xrDoJoht1dj+W504op4QaxhVT6z5W0RgzhKRKIhhOX18+1CsGNOlytSYypm3IHFEPSHWMKpK7Plb5PIkN7K0fPlytGjRAl5eXoiJiUFSUlKNbfv16weZTFbla/DgwYY248ePr3J7fHy8PS6FyEAsy+nj24fiwKz++GpST3w4OhpfTeqJA7P6O2egBDimnhBrGBnT52/dOdqm34Mudbtj+kV0G0mNLH399deYPn06Vq5ciZiYGCxbtgyDBg1Ceno6goODq7T//vvvUV5ebvg5Ly8PnTp1wmOPPWbULj4+HmvXrjX87OnpabuLIKqGfjl9bTlD9lhOr58acwn6ekLqLFS/D9rf+UPWrCfkiMcUq1r3oJPp9qBrO5j5W+RQkhpZ+uCDDzBp0iRMmDABUVFRWLlyJXx8fLBmzZpq2zdq1AgqlcrwtWvXLvj4+FQJljw9PY3aNWzY0B6XQ2TgqJwhl+eIekKsYfQP5m+RREgmWCovL0dycjLi4uIMx+RyOeLi4pCYmGjWOVavXo3Ro0ejQYMGRsf379+P4OBgtGnTBlOmTEFeXp7J85SVlUGtVht9EdWXPmdI5W881aby97Jp2QCXp68npLzj+VWG2W4JvyMeU4wckb/FRHKqA8lMw924cQMajQYhIcbz+CEhIThz5kyt909KSsKpU6ewevVqo+Px8fEYPnw4IiIikJGRgX//+9944IEHkJiYCIWi+k92ixYtwoIFC+p+MUQ1iG8fivujVI5dTu+KoobqpnrsWU/IEY8pNvbO32IiOdWRZOosXbt2DU2aNMGhQ4cQGxtrOD5z5kz88ssvOHz4sMn7P/vss0hMTMSJEydMtjt//jxatWqF3bt3Y8CAAdW2KSsrQ1lZmeFntVqN8PBw1lkiIrKEoeZULflb1qg5JaVCoGQ3TldnKTAwEAqFAjk5xsOxOTk5UKlUJu9bXFyMTZs2YeLEibU+TsuWLREYGIhz587V2MbT0xNKpdLoi4iILGSv/K1aE8mhSyTnlBzVQDLBkoeHB7p27Yo9e/YYjmm1WuzZs8dopKk63377LcrKyjBmzJhaH+fKlSvIy8tDaCjzQ0RDLDkGYukHkTOxR/4WE8mpniSTswQA06dPx7hx49CtWzf06NEDy5YtQ3FxMSZMmAAAGDt2LJo0aYJFixYZ3W/16tV4+OGH0bix8XLooqIiLFiwACNGjIBKpUJGRgZmzpyJyMhIDBo0yG7XRSaIJcdALP2wEYdvO2IDznhNTsvW+VssBEr1JKlgadSoUbh+/TrmzZuH7OxsREdHIyEhwZD0fenSJcjlxoNl6enpOHDgAH7++ecq51MoFDhx4gTWr1+PgoIChIWFYeDAgVi4cCFrLYlBTTkG+mJ19soxEEs/6sCcgEE0G9pakTNek6UkFyzKFUBEH9ucm4VAqZ4kk+AtZtxI1wbEstmoWPpRB+YEDAmnsjBlw7GaUl4lWbLAGa/JUgwW72DPRHKSFKdL8CY7EkNujlhyDMTSDwvpA4Y7N+fNLizFlA3HkHAqCxqtgAU7Uk2lvGLBjlRotJZ9ntJoBSRm5GFbylUkZuRZfP/6sNU1SYk5r73LYSFQqidJTcORHYglN0csOQZi6YcFagsYZNAFDH5e7lXeUO9sm1VYiqTMfLO3P3H0iEZSZr7Vr0lKzH3t749SiXtKzhb0ieTV/n1bLNqpdBIHBkv0DzHl5oglx0As/bCAuQFDYobpSvV6uTdrPtftapr+0o9o2GP6y9y+mttOahwdLIo+T4qFQKmOGCyRjtg2tBTLZqNi6YcFzA8EzJuKCvbzqrWNWEY0zOmrJe2kxpHBoqNHFc1my0RyclrMWSIdseXmiCXHQCz9sIC5gUBsy0CE+ntVuSo9GXRvdj0iGtV6LktGNGypR0Qjq12TFDkqWGSelJ2JIa/UxTBYIh0x5uaIZbNRsfTDTOYGDD1bNcb8IVGGY3e2AYD5Q6LMGgkSy/SXQi6z2jVJkSOCRSbV21nqdt3KvvUPAd9N1H1f1l53nGyGwRLpiDU3J2ooMO0UMO4HYMRq3fdpJ+0foIilH2awJGCIbx+KFWO6IERpXFcsROlpUY6RmKa/9Nek8jd+LJW/l9OXDXBEsCiWUUWXoM8rvXMWQJ9XyoDJZpizRDpizs0RS46BWPphBn3AcGcOiarGHJKa3lrNox/RyC4srel/D1R2nP6Kbx+K+6NU4k42thHLX/v6EcuootMTW16pi2GwRDr63JxvxkL31nb7L6Q4c3PINHMChppWsOWoLVvBph/RmLLhWE3/e+w+/aWQy5yyPIA57BksimlU0aa0GseuorMkr1QiH+qkhMES/cNJ65CIfjmzDZkKGKy9gs3eIxpkmr2CRbGNKtqEGOrPiTGv1IUwWCJjTlaHRDLLmR3AFjV5XHn6y1WJcVTRqsRSf06seaUuggneVJU+N6fDo7rvEg6UuJy5ZrbKNdGPaAyLboLYVo2l+yZJZnPapPpa84SgyxOyx9J9fV6pqbWOyiaiqvnmTDiyRE5JLEUSxcxlck1cgaPzaeCko4piyhNiXqlDMVgip+TobR+kwCVyTVyBGPJp/uZ0SfViyxNy0rxSKWCwRE6Jy5lr5/S5Jq5ALPk0zkqMeUJOllcqFcxZIrvRaAUkZuRhW8pVJGbk2bSiL6eYzOO0uSauQEz5NGJX1+1BxJon5CR5pVLCkSWyC3uvSuMUk/mcMtfEFYgpn0bM6jNNyTwh+htHlsjmHLEqzdX3CLMUV7BJkNjyacTIGtuDSGxvSLINjiyRTTlyVRqLJJJTMzNP5kC2AgqfPNcbLbTm9iDME3J5DJbIphy9Ko1TTGRNoqoGX8t+jloA2UJjjN3jBu2e312vGKu1pykltDckWR+DJbIpMaxKc7rlzOQQoqsGbyKfRr92YkHFU9D+nW2hn/Z2mcR9TlOSFTFniWyKq9LIGYi2GnwN+TTZaIwpFdPwk7aH4Zg+lFqwI9WmK1FFQ4zL/kmyOLJENsVVaSR1oq8Gf1s+zZ8Z5zBv7w0kadsaRpRu51LFWGuZptQt+w/j9iBkFo4skU1xVZrrsWc9LXuwJO/OYf7Op0kLHIjftVHVBkq3c4lirPppSgA1/vXhsn8yE0eWyOa4Ks11iC6vxwrEkHdnLk5734Hbg5CVMFgiu+CqNGkzZxWYPq/nznEkqScWSykA4bR3Nbjsn6yAwZKrs+Nu5VyVJk3mjBaJPq+nHswNQLRaAdtSrjr0gwD3+6sBl/1TPckEQZB2QoEIqNVq+Pv7o7CwEEql0tHdMZ+IdisncapptEj/VqsfLUrMyMPjq36v9XxfTeopyYBZ/zwAVQMQAUCAjzsKSioMxx099eiM06FEtmDu+zeDJSuQZLBU027l+rdBlvF3eRqtgN5L9taY3KwfUTkwqz9+OHENL21KqfWcH46OxrDoJtbtqJ1UF4A09HHHX7cFSXp3BpOOIKoCmkQiZe77N6fhXJE1twEgp2XJKjAp5fXU1Z15d4G+nnjlm5Rq25o79WjLgIbT3kTWw2DJFXG3cjKDJavAHuoY5hKJxbcHIIkZechWl9XYtraaRpwqI5IO1llyRdwGgMxgyWiRK9bTqk9JAdFWBCeiajFYckXcBsDqnK0QI/DPKrCawhsZdCMh+tEifT0tlb9xkKXy95Js2QBT6jr1WNvKQcCFtiQhXVpE5m/Ayc2671qNo3tE1eA0nCviNgBW5azTKXVZhu5K9bTqWtPIklww5hw5Oa5IlgzJjSwtX74cLVq0gJeXF2JiYpCUlFRj23Xr1kEmkxl9eXkZf8oTBAHz5s1DaGgovL29ERcXh7Nnz9r6MhxLDNsAOMmnKWefTqnLaJE+r2dYdBPEtmrslIESUPetfKRUEZxsSL8i+c78UXWW7njqdsf0i6olqZGlr7/+GtOnT8fKlSsRExODZcuWYdCgQUhPT0dwcHC191EqlUhPTzf8LJMZ/+F699138dFHH2H9+vWIiIjA3LlzMWjQIKSmplYJrJyKI7cBcJJPU85ciPF2rjRaZKm6bOXjCisHbckpSiJwRbLkSKrOUkxMDLp3745PPvkEAKDVahEeHo4XXngBs2fPrtJ+3bp1mDZtGgoKCqo9nyAICAsLwyuvvIIZM2YAAAoLCxESEoJ169Zh9OjRZvVLknWW9OxYwRuAU9V3cvZCjGQ+S97A9fWrapu+OzCrv/SCABtzminvzN+A9Q/V3m7cD1yRbGPmvn9LZhquvLwcycnJiIuLMxyTy+WIi4tDYmJijfcrKipC8+bNER4ejmHDhuH06dOG2zIzM5GdnW10Tn9/f8TExJg8Z1lZGdRqtdGXZOm3AejwqO67rafeTH6agu7TlESm5DidQnqWTD264spBa3CqKW+uSJYcyQRLN27cgEajQUiI8QqtkJAQZGdnV3ufNm3aYM2aNdi2bRs2bNgArVaLXr164cqVKwBguJ8l5wSARYsWwd/f3/AVHh5en0tzHZbUd5IAe0ynOOMqO3K9lYP15XQrCLkiWXIklbNkqdjYWMTGxhp+7tWrF9q1a4fPPvsMCxcurPN558yZg+nTpxt+VqvVDJjMYaNPU47KYbD1Du9OM+VA1WIumPmcbgUhVyRLjmSCpcDAQCgUCuTkGL+R5uTkQKVSmXUOd3d3dO7cGefOnQMAw/1ycnIQGvrPm09OTg6io6NrPI+npyc8PT0tvAKyxacpRwYUttzhvaYNbPVTDhx9cA7cksQ8TjflrV+R/M1YoKa/HrZekUwWkcw0nIeHB7p27Yo9e/YYjmm1WuzZs8do9MgUjUaDkydPGgKjiIgIqFQqo3Oq1WocPnzY7HOSBfSfpkyVOVQ2MfvTlBhyGGwxneJ0Uw5E9eSUKwj1K5KVd/yNUIZJaqGLq5DMyBIATJ8+HePGjUO3bt3Qo0cPLFu2DMXFxZgwYQIAYOzYsWjSpAkWLVoEAHjzzTfRs2dPREZGoqCgAO+99x4uXryIZ555BoCujMC0adPw1ltvoXXr1obSAWFhYXj44YcddZnOy4qfpsS0bN/a0ylON+VAVE+2nvJ2mKihuvIA9lyRTHUiqWBp1KhRuH79OubNm4fs7GxER0cjISHBkKB96dIlyOX/DJb99ddfmDRpErKzs9GwYUN07doVhw4dQlRUlKHNzJkzUVxcjMmTJ6OgoAC9e/dGQkKCc9dYciQr1XcSW0BhzekUp5tyIKonW05524rZuZT6FckkapKqsyRWkq6z5Cj1rO+0LeUqXtqUUmu7D0dHY1h0k3p01P5Yv4moelJZ9CCVfpL579+SGlkiJ1LPT1NOmcPwN6edciCqJymsIOTiDOckmQRvotvpAwoTqeIIlWhAwaKFRDUT896DXJzhvBgskSQ5e0DBooVE0mNJLiVJC6fhSLLqsomplEhhyoGI/sHFGc6LwRJJmrMHFCxaSCQdzpxL6eoYLJHkMaAgIjHg4gznxZwlIhI1biZMUuHsuZSujCNLRCRarFdDUuPsuZSuikUprYBFKYmsr6Z6NfrP5FwVSGJmdgVvcigWpSQiyRLT3n9EdcFcSufCnCUiEh3WqyEiMWGwRESiw3o1RCQmnIYjItFhvRoiB6vnZufOhsESEYkO69UQOVDqdiBhFqC+9s8xZRgQvwSIGuq4fjkQp+GIXIDUahWxXg2Rg6RuB74ZaxwoAYA6S3c8dbtj+uVgLB1gBSwdQGIm5VpFUu47keRoNcCy9lUDJQOZboRp2kmnmZIz9/2bwZIVMFgisXKGWkWsV0NkJ5m/Aesfqr3duB+AiD62748dsM4SkYtzllpFrFdDZCdFOdZt50SYs0Q2IbUcGWfEWkVEZBHfEOu2cyIcWSKrY56JOLBWERFZpHkvXU6SOguoaR2qMkzXzsVwZImsSp8jc+eIRnZhKaZsOIaEU1kO6pnrYa0iIrKIXKErDwCgxnWo8YudJrnbEgyWxEqr0SXbndys+67VOLpHtaotRwbQ5cg4+5ScWKYg9bWKaspGkkE34sdaRURkEDUUGPkFoLxjFkAZpjvuonWWOA0nRhItCGZJjoyzJuyKaQpSX6toyoZjkMF4UJ21ioioRlFDgbaDWcH7NhxZEhsJFwRz9RwZMU5BxrcPxYoxXaDyN55qU/l7SaJsABE5iFyhKw/Q4VHddxcOlACOLImLVqMbUTK12Dthti7iF+F/XFfOkRHzMv349qG4P0rFWkVERHXEYElMLh4yUTkVAARAfVXXztyCYHbcDNGV9/MS+xQkaxUREdUdgyUxsXZBMDvnPrlyjoyrT0ESETkz5iyJiTULgjko98lVc2RceQqSiMjZcWRJTKxVEMzBuU+umCPjylOQRGRndkyvIB0GS2KiLwj2zVigpokscwqC2SL3yUKuliPjylOQRGLgMhsuS7S0jNQxWBIbfUGwan8ZFpv3y8DNEB1CPwV5Z50lFbd6IbIpMdU3syl9esWd49f69AoXLhppazJBEJy7nLIdqNVq+Pv7o7CwEEql0jonrc8wa+ZvwPqHam837gebjSy5Mpf5hEskAvr6Zne+kel/45wmV1KrAZa1NzFr8HeaxrSTnJKzgLnv3xxZEit9QbC64GaIDuVqU5BEjiLm+mZWJ4L0ClcmudVwy5cvR4sWLeDl5YWYmBgkJSXV2HbVqlXo06cPGjZsiIYNGyIuLq5K+/Hjx0Mmkxl9xcfH2/oybIubIRKRC7CkvpnkMb3CoSQVLH399deYPn065s+fj2PHjqFTp04YNGgQcnNzq22/f/9+PP7449i3bx8SExMRHh6OgQMH4urVq0bt4uPjkZWVZfj66quv7HE5tsXNEInIyblUfTNrlpYhi0kqZykmJgbdu3fHJ598AgDQarUIDw/HCy+8gNmzZ9d6f41Gg4YNG+KTTz7B2LFjAehGlgoKCrB169Y698smOUvWwiWmROSkEjPy8Piq32tt99WkntKfGjfkLNWSXsGcJYuY+/4tmZGl8vJyJCcnIy4uznBMLpcjLi4OiYmJZp2jpKQEFRUVaNTIuNbN/v37ERwcjDZt2mDKlCnIy8uzat8dipshEpGT0tc3qykbSQbdqjinqG/G9AqHkkywdOPGDWg0GoSEGA8xhoSEIDs726xzzJo1C2FhYUYBV3x8PL744gvs2bMHS5YswS+//IIHHngAGo2mxvOUlZVBrVYbfRERkX3p65sBNYYPzlXfjOkVDmP2ariKigq89tpr+P7779GoUSM899xzePrppw235+TkICwszGSQ4UiLFy/Gpk2bsH//fnh5/bPlxOjRow3/7tChAzp27IhWrVph//79GDBgQLXnWrRoERYsWGDzPpN9cck/kfS4XH2zqKG63ReYXmFXZgdLb7/9Nr744gvMmDEDBQUFmD59Og4fPozPPvvM0MaW6U+BgYFQKBTIyTHO9M/JyYFKpTJ536VLl2Lx4sXYvXs3OnbsaLJty5YtERgYiHPnztUYLM2ZMwfTp083/KxWqxEeHm7mlZAYuUxROyIn5HJbLNWntAzVidnTcBs3bsT//d//YcaMGXjrrbdw9OhR7N27FxMmTDAESTKZ7f5jenh4oGvXrtizZ4/hmFarxZ49exAbG1vj/d59910sXLgQCQkJ6NatW62Pc+XKFeTl5SE0tOY3SE9PTyiVSqMvsi+NVkBiRh62pVxFYkYeNNq6B+r6onZ3LkHOLizFlA3HkHAqq77dJSIb09c3GxbdBLGtGjtvoEQOYfbI0tWrV9G+fXvDz5GRkdi/fz/69++Pp556Cu+++65NOni76dOnY9y4cejWrRt69OiBZcuWobi4GBMmTAAAjB07Fk2aNMGiRYsAAEuWLMG8efPw5ZdfokWLFobcJl9fX/j6+qKoqAgLFizAiBEjoFKpkJGRgZkzZyIyMhKDBg2y+fVQ3VhzFMilitoREVGdmD2ypA8mbtekSRPs27cPR44cwfjx463dtypGjRqFpUuXYt68eYiOjkZKSgoSEhIMSd+XLl1CVtY/owArVqxAeXk5Hn30UYSGhhq+li5dCgBQKBQ4ceIEhg4dirvuugsTJ05E165d8dtvv8HT09Pm10OWs/YokEsVtSMiojoxu87SM888A0EQsHr16iq3Xb16Ff369cP58+dFm+BtS6Kus+RENFoBvZfsrTG4kUGX1HlgVn+zR4G2pVzFS5tSam334ehoDItuYkFviYhI7Ky+N9zcuXNx5syZam9r0qQJfvnlF+zatcvynhKZyZJRIHML0AX7edXeyIJ2RETkfMyehmvevDkGDRqEX3/9FZWVlVVuDw4ORkREhFU7R3Q7W2xt4FJF7YiIqE4sLkp53333IT+/av5GYWEh7rvvPqt0iqg6thgFcpWidtZcPUhE5GrMnobTEwSh2hIBeXl5aNCggVU6RVQd/ShQdmFpTTsjQVWHUSBnL2rHGlJERPVjdrA0fPhwALpaSuPHjzdaLabRaHDixAn06tXL+j0k+pt+FGjKhmOQwXgryfqOAjlrUTv96sE7g0v96sEVY7owYCIiqoXZwZK/vz8A3ciSn58fvL29Dbd5eHigZ8+emDRpkvV7SHQbW44C6YvaOQvWkCIyjVsckbnMDpbWrl0LAGjRogVmzJjBKTdyGGcdBbI2W6weJHIWnJ4mS1icszRz5kyjPeAuXryILVu2ICoqCgMHDrRq54hq4myjQLZgi9WDRM6A09NkKYtXww0bNgxffPEFAKCgoAA9evTA+++/j2HDhmHFihVW7yAR1Q1rSBFVVdv0NKCbnuaKUbqdxcHSsWPH0KePbrfjzZs3Q6VS4eLFi/jiiy/w0UcfWb2DRFQ3rCFFVBW3OKK6sDhYKikpgZ+fHwDg559/xvDhwyGXy9GzZ09cvHjR6h0korpxlRpSRJbg9DTVhcXBUmRkJLZu3YrLly/jp59+MuQp5ebmcl80IpHRrx5U+RtPtan8vZiXQS6J09NUFxYneM+bNw9PPPEEXn75ZfTv3x+xsbEAdKNMnTt3tnoHiah+uHqQ6B+2Km5Lzk0m3L60zUzZ2dnIyspCp06dIJfrBqeSkpKgVCrRtm1bq3dS7MzdtZiIiBxPvxoOqL64LUddXYe5798WT8MBgEqlgp+fH3bt2oVbt24BALp37+6SgRIREUkLp6fJUhZPw+Xl5WHkyJHYt28fZDIZzp49i5YtW2LixIlo2LAh3n//fVv0kyQmv7gcP57Mws3SSkzp18rR3SEiMsLpaYnQaoCLh4CiHMA3BGjeC5Ar7N4Ni4Oll19+Ge7u7rh06RLatWtnOD5q1ChMnz6dwRIBAC7kFeP1rafg6+mGib0j4OFWp0FMIiKbYXFbkUvdDiTMAtTX/jmmDAPilwBRQ+3aFYvfwX7++WcsWbIETZs2NTreunVrlg4gg+imAQjy80RRWSV+P5/n6O4QEZGUpG4HvhlrHCgBgDpLdzx1u127Y3GwVFxcDB8fnyrH8/Pz4enpaZVOkfTJ5TLEtQsGAOxKzXFwb4iIyKq0GiDzN+DkZt13rca6506YBZiqs54w27qPWQuLg6U+ffoYtjsBAJlMBq1Wi3fffRf33XefVTtH0nZ/VAgAYHdaDuqw6JKIiMQodTuwrD2w/iHgu4m678vaW2+05+KhqiNKRgRAfVXXzk4szll69913MWDAABw9ehTl5eWYOXMmTp8+jfz8fBw8eNAWfSSJ6tUqEN7uCmQVluLUVTU6NPV3dJeIiKg+9NNjd4766KfHRn5R/3yiIjNnI8xtZwUWjywplUqkpaWhd+/eGDZsGIqLizF8+HAcP34c7u7utugjSZSXuwL33hUIANiVxqk4IiJJs9f0mG+IddtZgcUjSxEREcjKysJrr71mdDwvLw9NmzaFRmO/OUQSv/ujVPjpdA52peZg+v13Obo7RERUV5ZMj0X0qfvjNO+lW/WmzkL1gZlMd3vzXnV/DAtZPLJUU+5JUVERvLy4lw4Z6982GHIZkJalxuX8Ekd3h4iI6spe02Nyha48AIAatwGPX2zXektmjyxNnz4dgC6he968eUYr4jQaDQ4fPozo6Gird5CkrVEDD3Rr3ghJF/KxOy0HE+6JcHSXiIioLuw5PRY1VJf/VG2dpcV2r7NkdrB0/PhxALqRpZMnT8LDw8Nwm4eHBzp16oQZM2ZYv4ckefdHhTBYIiKSOntPj0UNBdoOllYF73379gEAJkyYgA8//JAbxjozK5eXvz8qBG//mIbD5/NReKsC/t5cCEBEJDn66bFvxkI3HVbNNsTWnh6TK+qX/2QlFid4r1271hb9ILGwQXn5FoEN0DrYF2dzi7A/PRfDoptYqbOOodEK3E+KiFyTyKbH7MXiYImcmA3rZ8RFheBsbhF+Ts2RdLCUcCoLC3akIquw1HAs1N8L84dEWWWncgZiRCR6IpoesxeZwNLK9aZWq+Hv74/CwkLpTk9qNboKrDUuC/17LnrayTr9Qhy79BeGf3oIvp5uOPp6HLzcpfdLlXAqC1M2HKsyU68PZVaM6VKvgMnWgRgRERkz9/2bW8GTjo3Ly0c3DUCTAG8UlVXip9PZdeujA2m0AhbsSDVVig0LdqRCo63bZw99IHZ7oAQA2YWlmLLhGBJOZdXpvEREVH8MlkjHxvUz5HIZRnRtCgD45ujlOp3DkZIy86sEMrcTAGQVliIpM9/ic9s6ECNyFI1WQGJGHralXEViRh7/D0uVLTfNlQjmLJGOHepnPNa1KT7eexYHz+Xhwo1iZBWWSiY3J/dmzYFSXdrdzpJALLZVY4vPT+QInFZ2EjZY9CNFDJZIxw71M8Ib+eCeVoE4cO4GBn/8G4rL/vl0IvY/osF+5lWnN7fd7WwZiBE5Qk35ffpp5frm95Gd2GPTXImQ3DTc8uXL0aJFC3h5eSEmJgZJSUkm23/77bdo27YtvLy80KFDB/z4449GtwuCgHnz5iE0NBTe3t6Ii4vD2bNnbXkJ4mSn8vJ3h/kBgFGgBIg/N6dHRCOE+ntVeWb0ZNAFfD0iGll8blsGYkT2xmllJ2GvTXMlQlLB0tdff43p06dj/vz5OHbsGDp16oRBgwYhNze32vaHDh3C448/jokTJ+L48eN4+OGH8fDDD+PUqVOGNu+++y4++ugjrFy5EocPH0aDBg0waNAglJa64Kd4ff0M5R2f+JRhVvkEodEK2JZSfRK52P+IKuQyzB8SBaDGUBLzh0TVaSrRloEYkb3ZMr+P7MjGi36kRlLB0gcffIBJkyZhwoQJiIqKwsqVK+Hj44M1a9ZU2/7DDz9EfHw8Xn31VbRr1w4LFy5Ely5d8MknnwDQjSotW7YMr7/+OoYNG4aOHTviiy++wLVr17B161Y7XpmIRA0Fpp0Cxv0AjFit+z7tpFWGWpMy85GtLqvxdrH/EY1vH4oVY7pA5W88wqPy96rXtIKtAjEm15IjcFrZSdhr01yJkEzOUnl5OZKTkzFnzhzDMblcjri4OCQmJlZ7n8TERMMGwHqDBg0yBEKZmZnIzs5GXFyc4XZ/f3/ExMQgMTERo0ePrva8ZWVlKCv7501frVbX9bLEyUbl5Z3hj2h8+1DcH6WyeuFIfSB2Z0Ksqo65XEyuJUfhtLKTsOemuRIgmWDpxo0b0Gg0CAkxfmFCQkJw5syZau+TnZ1dbfvs7GzD7fpjNbWpzqJFi7BgwQKLr8HVOcsfUYVcZpNVadYKxJhcS46kn1bOLiytaakIVJxWFj97b5orcpKahhOLOXPmoLCw0PB1+bL06gY5AnNzaqcPxIZFN0Fsq8Z1mnpjci05ki3z+2yCNYSqZ6dFP1IhmWApMDAQCoUCOTnG86M5OTlQqVTV3kelUplsr/9uyTkBwNPTE0ql0uiLaie5P6ISxORaEgNb5fdZXep23TZP6x8Cvpuo+76sve442XzRj5RIZhrOw8MDXbt2xZ49e/Dwww8DALRaLfbs2YOpU6dWe5/Y2Fjs2bMH06ZNMxzbtWsXYmNjAQARERFQqVTYs2cPoqOjAejyjw4fPowpU6bY8nJcVk25OQE+7lg0vIN4/ohKlDPkhZFzsFV+n9WwhpB5XHDT3OpIJlgCgOnTp2PcuHHo1q0bevTogWXLlqG4uBgTJkwAAIwdOxZNmjTBokWLAAAvvfQS+vbti/fffx+DBw/Gpk2bcPToUXz++ecAAJlMhmnTpuGtt95C69atERERgblz5yIsLMwQkJH13f5H9LNfM7A//ToCfT0R1841EgVtyVnywsg52Cq/r95qrSEk09UQajvY5YKCatlo0Y+USCpYGjVqFK5fv4558+YhOzsb0dHRSEhIMCRoX7p0CXL5PzOLvXr1wpdffonXX38d//73v9G6dWts3boV7du3N7SZOXMmiouLMXnyZBQUFKB3795ISEiAlxffTGxJ/0c0KlSJvkv34WxuEb4+ehlPxjR3dNckjcm1RGawpIaQiwcJpCMTBIGZnvWkVqvh7++PwsJC5i/VwdqDmViwIxWBvh7YN6Mf/LzcHd0lcdBq6jT0rV8NBxh/btZPfogqZ4TIEU5u1uUo1WbEaqDDo7bvDzmMue/fkknwJokzseJkTM/maBnYADeKyrFif4YDOyki9Ug8lUxyLZGjsIYQWYgjS1bAkaVamLFr9a7UHEz64ig83OTY+0pfNG3o46DOikBNiaf6sSEzE081WkG8ybVEjqTV6D581FZDaNpJ5iw5OY4skTjo3/jvzA/Qrzj5e6Qkrl0werZshPJKLd77Kd0BHRUJK25eWd+aTUROizWEyEIMllyZrYuxWfDGL5PJ8PrgKMhkwLaUazh+6S/r9kUquHklkX24Qg0hFty0GkmthiMrMmNqrN4sXHHSvok/RnRpis3JV/DW/9Kw+blYyGQuNhrCzSuJ7MeZawjZ42+8C+HIkisyc2qs3urwxj9jYBt4uyuQfPEv7DxV8/58TouJp0T2pa8h1OFR3XdnCZTs8TfehTBYcjVWzImpVR3e+FX+Xph8b0sAwKKdaSirdLFhY/3mlaZ20FM2cZnNK4nIQvb8G+9CGCy5GnvmxNTxjf/Zvi0R7OeJy/m3sP7Qhfr3Q0qYeEpE9cG8R5tgsORq7JkTU8c3fh8PN8wY1AYA8PHec8gvLq9/X6TEFRJPicg2mPdoEwyWXI29c2Lq+MY/oktTRIUqcbO0Eh/u/tM6fZGSqKHAtFPAuB90VYTH/aCr+cJAiYhMYd6jTbAopRVIqiilo4qx1WHrjkPnbuCJ/zsMhVyGn6bdi8hgX+v1h4jIGbHgpkVYlJKq56icmDqsOOkVGYi4dsHQaAUs3plm3f4QETkj5j3aBIMlVyShnJg5D7aDm1yG3Wm5OHjuRv1PyCJtROTsJPQ3Xio4DWcFkpqGu10dd7W3tze2n8a6QxfQVuWHHS/0hruijjE+i7QRkQlOt5+iRP7GO5K5798MlqxAssGSRPxVXI5+S/ej8FYFXhzQGtPvv8vyk1hpc1oick4Jp7KwYEcqsgpLDcdC/b0wf0gU4tuHmrgnSRlzlshpNGzggYUPtwcAfLL3LJIv5lt2AhZpIyITEk5lYcqGY0aBEgBkF5ZiyoZjSDiV5aCekVgwWCJJGNopDI90bgKtAEz7OgU3SyvMvzOLtBFRDTRaAQt2pJr6KIUFO1Kh0XISxpUxWCLJWDDsbjQJ8Mbl/Ft4Y3uq+XdkkTaXp9EKSMzIw7aUq0jMyOMbHxkkZeZXGVG6nQAgq7AUSZkWjmiTU3FzdAeIzKX0csey0dEY9Vkivjt2Bf3bBmNwRzNyCVikzaUxF4VMyb1Zc6BUl3bknDiyRJLSvUUj/KtfJABgzvcncK3gVu134ua0Lou5KFSbYD8vq7Yj58RgiSTnpbjW6NTUH+rSSrzyzR/Q1jalwiJtLom5KGSOHhGNEOrvZeqjFEL9dWUEyHUxWCLJcVfI8Z9R0fB2VyDxfB5W/JJR+51YpE0U7Jk7xFwU8RJTDplCLsP8IVEAavwohflDoupVb0lM10t1w5wlkqSWQb54Y2gUZn13Eu//nI7OzQLQq1Wg6TtFDQXaDmaRNgexd+4Qc1HESYw5ZPHtQ7FiTJcq/VJZoV9ivF6yHItSWgGLUjqGIAh4dfMJbE6+gkBfD/zwQh+o/KWZV+B0lYPvoM8dqqEkKFaM6WL1N47EjDw8vur3Wtt9NaknYls1tupjU/Uc8f/AEtb+PRT79ZL5798cWSLJkslkWDisPU5fUyMtS43nvzyGTZN71n07FAdx9k+eteUOyaDLHbo/SmXVAFGfi5JdWFrT3utQMRfFbhz1/8ASCrnMaoGzFK6XzCetdxWiO3h7KLDiyS7w83JD8sW/sPAHC+oviYArrNZyVO6QPXJRyHyulkMmievlxuJmY7BEktcisAH+MzIaAPBF4kVsSrrk2A6ZyVVWazkyd0ifi3Ln9KzK34tTIHbmajlkor/e1O3AsvbA+oeA7ybqvi9rrztOVXAajupGZLtZx0WFYPr9d+GDXX/i9a2n0KyxT+0J3w5mySdPc6YGxJr35Og6NvHtQ3F/lEqUz40rcfT/A3sT9fXWtLG4Okt3nCuEq2CwRJZL3a7bmPb2/daUYbpaRg78BXuhfyTO5hZhxx/XMGXDMWz5Vy+0DPJ1WH9qY81PnmLOexJD7pA1c1GobsTw/8CeRHu9tW4sLtNtLN52MFcK34bTcGQZ/SeSOzem1X8iceAQrkwmw3uPdkTnZgEovFWBp9cdwV/F5Q7rT22s9clT7HlPzB2yLanU8HG1/weivV5uLF4nDJbIfLV+IoHuE4kDkwS93BX4/KluaBLgjQt5JXhuQzLKK7UO648p1qgcLJW8J+YO2UbCqSz0XrIXj6/6HS9tSsHjq35H7yV7HR4g18TV/h+I8nq5sXidsM6SFbhMnaXM33RJgLUZ9wMQ0cf2/TEhPfsmRqw4hKKySgzv0gTvP9YJMpn4PrHqR4UA4xDU3DosUqslJNa8KimScg0fV/t/IKrrldDfcXtgnSWyPgl9Immj8sMnT3TGxPVH8f2xqwjz98aMQW0c3a0q6ls5WPQrbu7A3CHrkHoNH7H9P7B1MCOq69VvLK7OQvWzBDLd7dxY3AiDJTKfb4h129lYvzbBeOeR9pj13Ul8su8cgvw8Ma5XC0d3q4r6rNYS9Yobshlrr6R0ZWJeHGET+o3FvxkLXVhdzZg2NxavQjI5S/n5+XjyySehVCoREBCAiRMnoqioyGT7F154AW3atIG3tzeaNWuGF198EYWFhUbtZDJZla9NmzbZ+nIco74FyPSfSExl2SibiOoTyajuzfBy3F0AgDd2nMa2lKvWObGVi7npP3kOi26C2FaNzf5Uyx3TXZPURhTFSuyLI2yGG4tbTDIjS08++SSysrKwa9cuVFRUYMKECZg8eTK+/PLLattfu3YN165dw9KlSxEVFYWLFy/iueeew7Vr17B582ajtmvXrkV8fLzh54CAAFteimNYY7m/RD+RvDggEnnFZfgi8SJe+eYP+Hq6YUC7eox+iah0gn7FzZQNx2p6RZxqhRHpcESx/qQ+lVlv3FjcIpJI8E5LS0NUVBSOHDmCbt26AQASEhLw4IMP4sqVKwgLCzPrPN9++y3GjBmD4uJiuLnp4kSZTIYtW7bg4YcfrnP/RJ/gXVMBMv3bqaWfJKoNFproAiWRfiLRagVM/yYFW1OuwcNNjnUTutetaKW1n0srcbmpBBen0QrovWRvrTV8Dszq75xv9FYgtcURZBtOleCdmJiIgIAAQ6AEAHFxcZDL5Th8+DAeeeQRs86jfzL0gZLe888/j2eeeQYtW7bEc889hwkTJphcOVVWVoaysjLDz2q12sIrsiNbFCCT4CcSuVyG9x7rhOJyDXal5mDS+qNY93QPdG9helm+UR5Rc38oRFrMzdZVqkW1moc4omgFnMokS0giWMrOzkZwcLDRMTc3NzRq1AjZ2dlmnePGjRtYuHAhJk+ebHT8zTffRP/+/eHj44Off/4Z//rXv1BUVIQXX3yxxnMtWrQICxYssPxCHMGSAmSWLBOVKyS3rNRdIcfHj3fGM+uP4sC5Gxi3Jglrx3dHTMuqnxqrG6l50O8cPq2wwXNpJbZaccNRK3Gq70pKV8epTLKEQxO8Z8+eXW2C9e1fZ86cqffjqNVqDB48GFFRUXjjjTeMbps7dy7uuecedO7cGbNmzcLMmTPx3nvvmTzfnDlzUFhYaPi6fPlyvftoMxJa7m8PXu4KrBrbDb0jA1FSrsH4tUeQmJFn1KampE+34lzzHsSJnkuXTYCViPj2oTgwqz++mtQTH46OxleTeuLArP4MlMxg6eKIkvJKTFibhKMX8u3WRxIPhwZLr7zyCtLS0kx+tWzZEiqVCrm5xm9UlZWVyM/Ph0qlMvkYN2/eRHx8PPz8/LBlyxa4u7ubbB8TE4MrV64YTbPdydPTE0ql0uhLtCS23N8evD0U+L9x3XDvXUG4VaHBhHVJOHjuBgDTSZ+5CDDvAZzkuZRKdXBXV9eVlK7O0u1IPtx9FvvSr2Pa1ymo0IhzVwCyHYdOwwUFBSEoKKjWdrGxsSgoKEBycjK6du0KANi7dy+0Wi1iYmJqvJ9arcagQYPg6emJ7du3w8ur9uHUlJQUNGzYEJ6enuZfiJixAFm1dNuidMWUDcnYl34dT687glVju8FdIa+xfk2Sti2uCY2gQj6qfz9yrueStXzI2Zk7lXnqaiH+70AmAGDhsPZwV0im6g5ZiSRyltq1a4f4+HhMmjQJK1euREVFBaZOnYrRo0cbVsJdvXoVAwYMwBdffIEePXpArVZj4MCBKCkpwYYNG6BWqw2J2EFBQVAoFNixYwdycnLQs2dPeHl5YdeuXXjnnXcwY8YMR16udUl0ub9VaDUmk9C93BVY+VRX/GvDMew5k4tnvjiKp+9pUfPpIMeCirFY4b4MAmSQOflzyQRYcgW1LY7QaAXM+f4kNFoBD3UMxX1tg2s5IzkjSQRLALBx40ZMnToVAwYMgFwux4gRI/DRRx8Zbq+oqEB6ejpKSkoAAMeOHcPhw4cBAJGRkUbnyszMRIsWLeDu7o7ly5fj5ZdfhiAIiIyMxAcffIBJkybZ78LsQV+ArNraQOJd7l8vZtZC8nRTYMWYrnj+y2PYlZqD//st0+Rpf9L2wJSKafgoYBM8S25bXOCEzyUTYMlVmFocse7QBZy8Wgillxvm/T1tR65HEnWWxE70dZb0ahlpcRp1qIVUXqnFi18dR8Jp06srDfVrXu0LxeVEp34uWcuHXN2Vv0ow8D+/oqRcg8XDO2B0j2aO7hJZmbnv35x4dSX65f4dHtV9d7I3dwBm1JWCrhbSHduTeLjJ8fETnTGkU80FTo2SPt3cnP65tDQBlsiZCIKAuVtPoaRcgx4RjTCyW7iju0QOxGCJnIsldaXu4K6Q48NR0Rhfw2a7Kn8vrBjTxaWWZesTYFX+xlNtrvhckGv54UQW9qVfh4dCjnce6QA5PxS4NMnkLBGZpZ51peR/j6YE+npg6c9/AgB6tmyEqfe1dtll2bauDk4kNoUlFViw4zQA4Pn7IhEZ7OvgHpGjMVgi52KFulIymQxT+7dGY19PvLblJH4/nw9Pt/OIbhYAX0/X/JWxVXVwIjFatDMNN4rKERnsi+f6tXR0d0gEOA1HzkVfV8pUXV5lE7NqIT3eoxk+e6obvNzl+OXP63hsZSKyCm9ZtbtEJC6/n8/DpiO6XRkWDe8ATzfny0ckyzFYIueirysFoMa0ZAtqId0fFYKvJ8ci0NcTaVlqPLz8IE5eKbRad4lIPEorNPj3lpMAgCdimpncaJtcC4Mlcj76ulLKO5KPlWHVlg2oTafwAGz5Vy+0DvZFjroMj312CDtPck80Imfz5g+pOH+9GEF+npgV39bR3SERYZ0lK5BMnSVXY+W6UurSCkz98jh+/fM6AOClAa3x0oDWXCVD5AS2pVzFS5tSIJMB/306Br1bBzq6S2QHrLNEZOW6Ukovd6wZ1w0T/t4S5cM9ZzFlYzJullZYobNE5Cjp2Tcx+zvd9Nvz/SIZKFEVDJaILOCmkGP+kLvx7qMd4aGQ46fTOXh4+UGcyy1ydNesT6sBMn8DTm7Wfb+jkCeRMygsqcBzG5Jxq0KD3pGBePn+uxzdpfrh761NuOY6aKJ6GtktHK2DfTFlwzFkXC/Gw8sPYuljnRDfXuXorlmHmXvrEUlZpUaLFzYdR+aNYjQJ8MZHj3eWdv0w/t7aDEeWiOqoc7OG2PFCb/SIaISisko8tyEZ7/yYhgqN1tFdqx/93np3VkJXZ+mOp253TL+IrEgQBCzYkYpf/7wOb3cFPh/bFY0aeDi6W3XH31ubYrBEVA9Bfp7Y+EwMnukdAQD4/NfzGPVZIq4VSLQeUx331iOSmlW/ncd/f78ImQz4YGQn3B3m7+gu1R1/b22OwRJRPbkr5Hj9oSisHNMFfl5uOHapAA9+9Bt2pZq59YqY1GNvPSKp+PFkFt758QwA4LUH2+GBDhLf45C/tzbHYInISuLbh+J/L/RBx6b+KCipwKQvjmLGt39ALaXVchburafRCkjMyMO2lKtIzMiDRstKJCRuB8/dwLSvUwAA42KbY+Lfo8KSVs89Mal2TPAmsqJmjX3w7XOxeP/nP7Hqt/PYnHwFB8/dwJIRHXHvXUGO7l7tLNhbL+FUFhbsSEVWYanhcKi/F+YPiUJ8e4l/UiendOzSX5j0xVGUV2ox6O4QzBtyN2QyCSd061lhT0wyjSNLRFbm6abAvx9sh2+fjUWLxj7IKizF2DVJeG3LSRSXVTq6e6aZubdeQlEEpmw4ZhQoAUB2YSmmbDiGhFO2r3DOUS2yRFqWGuPXJKGkXIM+rQOlv/LtdlbcE5OqxwreVsAK3i6gjtXAS8orsWTnGaxPvAgACG/kjfce7YSeLRvbusd1p19VA8A4YVT3h1jz2Hr03u5bJVC6vZXK3wsHZvW32ZuRM41qabQCkjLzkXuzFMF+XugR0ch53sRFIvWaGmNWH0Z+cTm6Nm+I/07sAR8PJ5tYqeX3ti5bPbkCc9+/GSxZAYMlC1l5GxKbs0LtkkPnbuDVzSdwteAWZDJgQq8IzIxvAy93kV53tdfcBIhfjETPe/D4qt9rPcVXk3oitpX1g8KEU1mYsuFYlXU/+vBixZgukgmYnCnoM8mBv/OnrhZizOrDKCipQMem/vjvxBj4e7vb5bHtzsTvLQOl6jFYsiMGSxaQWtE0w6e1Gt6aLfi0drO0Am//Lw2bjlwGALQMbIClIzuhS7OG1uuvNdXwBqffQ6s2H46OxrDoJlbtkkYroPeSvQ4d1bIWZwr6THLg7/wflwswdk0SCm9VIDo8AOuf7uG8gZKe1D6MOhj3hiPxkVrRNCvXLvHzcsfiER2xdkJ3hCg9cf5GMR5dcQhLEs6grFKE9U9q2Fsv2M/LrLub284SSZn5NQZKgO5VySosRVJmvtUf25o0Wl1BRBP/s7BgR6r087Ac+Dv/y5/X8fiq31F4qwKdmwXgi4kuECgBVt8Tk3QYLJF9SLFomo1ql9zXJhg/T+uLRzo3gVYAVuzPwNCPD+LU1cL69ddOekQ0Qqi/l6lUUoT663JvrC33Zs2BUl3aOYqzBH0mOfB3fsvxK5i47ghKynX7vf13YgyUXi4QKJHNMFgi+5Bi0TQb1i7x93HHf0ZFY+WYrmjcwAPpOTfx8PKDWLb7T5RXinu7FIVchvlDogBUXXuj/3n+kCibTIM5clTLmpwl6DPJQb/zq349j5e//gOVWgFDO4Vhzfju8PV0smRusjsGS2QfUiyaZofaJfHtVfj55XvxQHsVKrUClu0+i8Ef/YbD5/PqfE6buW038/gG57DiyU5Q+RsHJSp/L5vm2jhyVMuanCXoM8nOv/PllVrM23YKb/+YBgCY2DsCy0ZFw8ONb3NUfwy3yT6kWDRNX7tEnYXqpxJkutvrWbuksa8nPn2yC7b/cQ1v7kjF2dwijPr8dwzv3ASzH2wrjjfMapJ045VhuH/oYiR59bbbsnf9qNaUDccgQ7ULpG02qmVN+qAvu7C0pv9ZUEkg6DPJjr/zuTdL8fzGYzhy4S8AwOwH2uLZe1s6R8FJEgWG3GQfUiyaJlfoVuwAqHHCKX6xVRIoZTIZhkU3wZ5X+uKJmGaQyYDvj19F/6W/4P9+O+/YqTkTSbqKb8chtuwghkU3QWyrxnYJUuLbh2LFmC52H9WyJkdOZdqNnX7nj136C0M+PoAjF/6Cn6cbVo3thuf6tmKgRFbF0gFWwNIBZpJq0TQH1C5JuVyA+dtO4Y8ruqTvlkEN8NqD7dC/bbB93wS0GmBZexO5J3+Prk07afdVN85QzNHp6yzZ+Hf+y8OXMH/7KVRoBEQG++Kzp7qiVZBvnc9Hrod1luyIwZIFpFo0zQG1S7RaAZuTr2BJwhnkFZcDAHpHBuL1h9qhrcpO/88yfwPWP1R7u3E/6JYpk8WcIegzyQa/82WVGszfdtpQsyz+bhWWjuzERG6yGIMlO2KwZCEWTbOIurQCy/edw9oDF1Cu0UIuA0Z2C8dLca0R6u9t2wc/uRn4bmLt7Uas1tV1IaqOFX/nr/xVgqlfHkfK5QLIZMCMgW3wr36cdqO6YbBkRwyWyB4u5ZVgcUIafjyZDQDwdJNjXK8WmNK3FRo28LDNg3JkiURCEARsTbmKeVtP42ZZJZRebvjo8c7o1ybY0V0jCWOwZEcMlsiejl7Ix5KEM4aVP36ebph0b0tM7B2BBtaehjDkLNWyItABOUvkOgpLKvDa1pP44UQWAKBzswB8OKozmjX2cXDPSOoYLNkRgyWyN0EQsD/9Ot79KR1pWWoAQKCvB56/LxJPxDSDp5sVAxepJuaTU/jlz+uYtfkEstWlUMhleGlAa/yrXyu4KbiYm+qPwZIdMVgiR9FqBew4cQ0f7PoTF/NKAOhWU03p1woju4XDy91KQZNUE/NJsgpKyrHwhzR8d+wKACAisAH+Myoa0eEBju0YORWn20g3Pz8fTz75JJRKJQICAjBx4kQUFRWZvE+/fv0gk8mMvp577jmjNpcuXcLgwYPh4+OD4OBgvPrqq6isrLTlpRBZjVyuq8+0e3pfvP1Ie4QoPZFVWIp5206j73v7sPZgJkorrLD3VtRQYNopXW7SiNW679NOMlAiqxMEAdtSriLug1/x3bErkMmACfe0wP9e7M1AiRxGMiNLDzzwALKysvDZZ5+hoqICEyZMQPfu3fHll1/WeJ9+/frhrrvuwptvvmk45uPjY4geNRoNoqOjoVKp8N577yErKwtjx47FpEmT8M4775jdN44skViUVmjw7dHL+HR/hqF2T6CvJyb2jsCTPZtxM1EStfPXizB32ykcPKfb7qdVUAO8+2hHdG0u4UrmJGpONQ2XlpaGqKgoHDlyBN26dQMAJCQk4MEHH8SVK1cQFhZW7f369euH6OhoLFu2rNrbd+7ciYceegjXrl1DSIiu5P7KlSsxa9YsXL9+HR4e5q0wYrBEYlNWqcF3yVexfN85XC24BUCXCP5ETDOM7dUCTQJsXHLAyTh9LSQHKygpx0d7zuGLxAuo1ArwcJPjhfsiMblvS+vm3xHdwamCpTVr1uCVV17BX3/9ZThWWVkJLy8vfPvtt3jkkUeqvV+/fv1w+vRpCIIAlUqFIUOGYO7cufDx0a2gmDdvHrZv346UlBTDfTIzM9GyZUscO3YMnTt3Nqt/DJZIrCo0WmxPuYaVv2TgbK5u2lohlyH+bhWe7t0CXZo1ZH2aWjh9lW0HqtBo8d/Ei/hwz1kU3qoAANzXJghvDL0bzRs3AMBA1STWrKs3c9+/JVHuNDs7G8HBxrU03Nzc0KhRI2RnZ9d4vyeeeALNmzdHWFgYTpw4gVmzZiE9PR3ff/+94bz6ESU9/c+mzltWVoaysjLDz2q12uJrIrIHd4UcI7o2xSOdm2Bfei7WHMzEwXN5+N/JLPzvZBY6hQfg6Xta4MEOoXDn6qIqEk5lYcqGY1WKJmQXlmLKhmOS2YtObARBwO60XCz6MQ3nbxQDANqE+OG1we1w711BhnYMVE2odtFFmG4/S+YSWp1Dg6XZs2djyZIlJtukpaXV+fyTJ082/LtDhw4IDQ3FgAEDkJGRgVatWtX5vIsWLcKCBQvqfH8ie5PLZRjQLgQD2oXgTLYaaw5kYmvKNfxxuQAvbUrBoh/P4KnY5niiRzPbFbiUGI1WwIIdqdVWlxKgK5ywYEcq7o9ScaTDTIIg4OC5PHywKx3HLhUA0JW8eGVgGzzWtalROQAGqiYYynnc8eyos3THWc7D6hwaLL3yyisYP368yTYtW7aESqVCbm6u0fHKykrk5+dDpVKZ/XgxMTEAgHPnzqFVq1ZQqVRISkoyapOTkwMAJs87Z84cTJ8+3fCzWq1GeHi42f0gcqS2KiXefbQTZsa3xZeHL+GLxIvIVpfivZ/S8eGesxjcIRRPxjRD1+auPUWXlJlvNKJxJwFAVmEpkjLzEduqsf06JkEarYB1BzPxZdIlZFzXjSR5usnxdO8I/KtfK/jdsfBAjIGqaKYDtRrdiJKpZydhNtB2MKfkrMihwVJQUBCCgoJqbRcbG4uCggIkJyeja9euAIC9e/dCq9UaAiBz6HOTQkNDDed9++23kZuba5jm27VrF5RKJaKiomo8j6enJzw9Pc1+XCIxCvT1xIsDWuPZvi3xvxNZWHMwE6euqrHl+FVsOX4VrYN9Map7OIZ3aYpGLjjalHuz5kCpLu1ckSAI+GDXn/jslwyUa/55c/fxUGD+Q1EY1aNZtfcTW6AqqunAi4eMp96qEAD1VV07bkFkNZJIUmjXrh3i4+MxadIkJCUl4eDBg5g6dSpGjx5tWAl39epVtG3b1jBSlJGRgYULFyI5ORkXLlzA9u3bMXbsWNx7773o2LEjAGDgwIGIiorCU089hT/++AM//fQTXn/9dTz//PMMhshleLopMLxLU+yY2hvbnr8HI7s1hZe7HGdzi/DW/9IQ885uTNmQjD1pOajUaB3dXbsJ9vOyajtXUl6pxebkK7hnyV58vPecUaAEALfKNZj9/UkknMqq9v5iClT104F3Bm/66cCarsFminKs247MIokEbwDYuHEjpk6digEDBkAul2PEiBH46KOPDLdXVFQgPT0dJSW6KsYeHh7YvXs3li1bhuLiYoSHh2PEiBF4/fXXDfdRKBT44YcfMGXKFMTGxqJBgwYYN26cUV0mIlchk8nQKTwAncID8PpDUdjxxzVsSrqMk1cLsfNUNnaeykagrweGdArDw9FN0LGpv1NP0/WIaIRQfy9kF5bWtCseVP666RjSyS4sxebky/jv7xeRoy6rsV1tU2liCVTFOB0I35Da21jSjswiidIBYsfSAeTMUq+p8d2xK9h6/CryissNx1s09sHQTmEYGt0EkcG+Duyh7ehHFYBqd8Vz7STjv5VXarH3TA6+PnIZv/x5Hdq/n6gAH3cUlFTUev+vJvWsMpWm0QrovWRvrYHqgVn9bRqkJGbk4fFVv9farrprqLeaygJwc2urcqrSAUTkOFFhSkSFRWH2A23x65/XseX4VexOy8GFvBJ8tPccPtp7Dm1VfniwQyge7BDqVIFTfPtQrBjTpUq+isrJl6+bk8x8Nucmvj5yGVvuCKJ7tGiEUd3DAQh45dsTtT5WdVNpCrkM84dEYcqGY5Ch+kB1/pAom4/mOGw6sLayAPFL/l4NV8OzE7+YgZKVMVgiIrO4K+SG8gPFZZXYlZqD7X9cw69/XseZ7Js4k30TH+z6E62CGmDQ3SoMuluFDk38IZf4svr49qG4P0oljpVQdmAqmfmeyED8cCILXx+5jJTLBYbbg/08MaJrUzzWtSlaBumC5cSMPLMer6apNDEEqg6ZDjS3LMDIL2oIqLi5tS1wGs4KOA1HrqygpBw/p+bgx5NZOHjuBipuS+YN8vPEgLbBuK9tMO6JDISvJz+fiVlNtY30PBRylP+d5O8ml6F/22CM6h6OvncFGdVIAqw3lebIJft2nw40TLHVtNrtjik2VvCuN6fa7kTsGCwR6ahLK7DvTC5+Pp2DX/68jqKySsNt7goZujVvhH5tgnDvXUFoE+IHObTS/GMv9TepavqvgRy9l+w1uWQf0G1uO6p7OB7p3BRBfqZXDTtDzpddryHzN2D9Q7W3G/cDywJYCYMlO2KwRFRVWaUGh8/nY09aDvb/eR0X80qMbg/0EhArpOAezRHEylPRTJYLmb8EtmuQ+jYTNfR/713z8PSB2v9+fflMDHpFBpr9cKKqUVRHdruGk5uB7ybW3m7EaqDDo9Z7XBfGYMmOGCwR1S7zRjF+Sc/F/j+v43BGLm5VGk9bBKIAXeVn0U3+J7rePxrt73kIHm4iKwVXUz6JfpxB7NtM3Nb/W4IHfte2wy/aTtinjcZFwbzdED4cHY1h0U0seljRVL+uB7tcA0eW7I7Bkh0xWCKygFaD8v90QkqhDw5o2uOgtj1OCK1Qccd6E083OTo1DUCX5g3RrXlDdG3e0LH71lmaTyIyeepbOLJ8LI4WBeGItg1OCy1Qedtz7o5KdJRl4JjQGoKJesU2WSZ/J6lPc9YVywLYHUsHEJE4XTwEj5uX0UMO9JCnYzq+Q6ngjpNCSxzV3oVkbWska9vgr0o/JF3IR9KFfMNdmwR44+4wJe4O89d9b6KESulln+KY9dxmwp6jK6UVGqRn38Spa4U4cbkQRy7m4/z1YgDjjdqFIg/9FH+gnzwF98hPwVdWitHlr+N3bdXtnuxWhFPq05z1IVewLIBIMVgiIvuqZhsGL1kFusvS0V2eDgAQBOD8wDVI9uiB5It/4ejFfGRcL8bVglu4WnALP6f+cw5/b3dEBvuiVVCDv7/7IjLYF00b+lg3GKnHNhO2ynkRBAHXb5bhz5winMlWIzVLjdRrapzNLYJGW3Vk4i7ZZXST657nbrJ0NJXdwJ1xZjAKHFfbyNxl886MZQFEicESEdmXGdswyGRAq7BgtIoIx8ju4QCAwlsVSL2mxulrhTj99/eM68UovFWB5It/IfniX0bn8HCTo2VgA7QMaoBmjRogvJE3wgK8EebvDZW/F5RebpaNSNVxm4maluPr9xYztZqqUqNFtroUV/+6hWuFt3D1r1t/B4yluPpXCa4VlOJWhaba+zZu4IG7m/ijfZgS3Vo0RBchHQFfz6q1+2PiuuPIYS/71zbSanQBgqnNRRJmA20HO//IStRQ3XW64lSkSDFYIiL7at5L9ym5tryM5r2Mjvp7uyO2VWOjfJnSCg3OXy/GuetFyMgtMnw/f6MY5ZVaQ7HM6jTwUEDl7wWVvxcCfT3RuIEnGvt6oHEDD/h7u8PPyx1Kbzf4ebnDz8sNyiY94WFhv2vbWwwA/r3lJDRaAX+VVODa3yNn1wp0gVG2uhTVDBAZkcuA5o0b4K4QX7RVKdGhiX/105PaQLOe9x79huBAP7n9E7LrOc3pdOQK17hOiWCCtxUwwZvIQobpFqDaCZ96TrdotAKu/nUL567fROaNElzKK8blv24hq7AUWYW3zNqzrDoecgHe2mJ4oAKesgp4oBJyaKGAADm0kDdsBq2nP7RaAVpBQHFZJa7VUreo1sdUyBEa4IUmAbqRsSYB3mjS8O/vAd4IDfCCp5uZIw42ft7rhcvmyQGY4E1E4mXjvAyFXIZmjX3QrLFPtbffKtcgW60LnHLUpcgrKseNonLcKCpDfnE5bpZWQH2rEjdLK3CztBI3/y6uWa6VoRx/731X3cfMfABQW9zfiMAGaBXki7Dbg6KG3mga4I1AX0/rbRkj5nyYOk5zEtkDR5asgCNLJApSXG4tkT5rtAKKyipRVFaJW+UalJVXoPxKCiqK/4LGqyG0QXdDAxm0ggC5TAaFXAaZDDiTdRNv/pBa6/ntshz/dmJ83rlsnhyAI0tErkSqy60lkpehkMvg7+0Of2/3fw42va/W+8VENMaq387XureYzZfj30mMzzuXzZOIiaw8LhFZTJ+HcmdyrH65dep2x/SLoJDLMH+IrmbRnRNpdluOLyX6aULlHavulGGuUTaARIvTcFbAaThyGIlXlXYVzrA/ml2JcZqQnBKn4YhcAZdbS0J8+1DcH6WS/P5odiPGaUJyaQyWiKSsHlWlyb4Ucpl9k7iJyGqYs0QkZVxuTURkcwyWiKRMXw27SvqwngxQNqlSDZuIiMzHYIlIyvTLrQHUuN6Ky62JiOqFwRKR1HG5NRGRTTHBm8gZcJdyIiKbYbBE5Cy43JqIyCY4DUdERERkAoMlIiIiIhMYLBERERGZwGCJiIiIyAQGS0REREQmMFgiIiIiMoHBEhEREZEJrLNERETOSathoVayCgZLRETkfFK3AwmzAPW1f44pw3R7KXILILKQZKbh8vPz8eSTT0KpVCIgIAATJ05EUVFRje0vXLgAmUxW7de3335raFfd7Zs2bbLHJRGRI2k1QOZvwMnNuu9ajaN7RNaSuh34ZqxxoAQA6izd8dTtjukXSZZMEATB0Z0wxwMPPICsrCx89tlnqKiowIQJE9C9e3d8+eWX1bbXaDS4fv260bHPP/8c7733HrKysuDr6wtAFyytXbsW8fHxhnYBAQHw8vIyu29qtRr+/v4oLCyEUqmsw9URkV1x1MF5aTXAsvZVAyUDme61nnaSU3Jk9vu3JKbh0tLSkJCQgCNHjqBbt24AgI8//hgPPvggli5dirCwsCr3USgUUKlURse2bNmCkSNHGgIlvYCAgCptichJ6UcdcMfnRP2ow8gvGDBJ2cVDJgIlABAA9VVdO+6lSGaSxDRcYmIiAgICDIESAMTFxUEul+Pw4cNmnSM5ORkpKSmYOHFilduef/55BAYGokePHlizZg1qG2wrKyuDWq02+iIiCdBqdCNKdwZKwD/HEmZLY0qO04jVK8qxbjsiSGRkKTs7G8HBwUbH3Nzc0KhRI2RnZ5t1jtWrV6Ndu3bo1auX0fE333wT/fv3h4+PD37++Wf861//QlFREV588cUaz7Vo0SIsWLDA8gshIsdyllEHTiPWzDfEuu2I4OCRpdmzZ9eYhK3/OnPmTL0f59atW/jyyy+rHVWaO3cu7rnnHnTu3BmzZs3CzJkz8d5775k835w5c1BYWGj4unz5cr37SER24AyjDkxeNq15L13gCFkNDWSAsomuHZGZHDqy9Morr2D8+PEm27Rs2RIqlQq5ublGxysrK5Gfn29WrtHmzZtRUlKCsWPH1to2JiYGCxcuRFlZGTw9Patt4+npWeNtRCRiUh91qHUaUaabRmw72HWTl+UK3QjbN2OhC5huf67+DqDiF7vu80N14tBgKSgoCEFBQbW2i42NRUFBAZKTk9G1a1cAwN69e6HVahETE1Pr/VevXo2hQ4ea9VgpKSlo2LAhgyEiZ6QfdVBnofqA4++VUmIddXCWaURbixqqS9SvdqpyMacqyWKSyFlq164d4uPjMWnSJKxcuRIVFRWYOnUqRo8ebVgJd/XqVQwYMABffPEFevToYbjvuXPn8Ouvv+LHH3+sct4dO3YgJycHPXv2hJeXF3bt2oV33nkHM2bMsNu1EZEdSX3UwRmmEe0laqhuhI0VvMkKJBEsAcDGjRsxdepUDBgwAHK5HCNGjMBHH31kuL2iogLp6ekoKSkxut+aNWvQtGlTDBw4sMo53d3dsXz5crz88ssQBAGRkZH44IMPMGnSJJtfDxE5iJRHHaQ+jWhvcoVrj7CR1UimKKWYsSglkQRJcd8wQ8HFWqYRWXCRyCxOVZSSiMjqpDjqIPVpRCKJkkRRSiIi+pt+GlEZanxcGcbq40Q2wpElIiKpYfIykV0xWCIikiIpTiMSSRSn4YiIiIhMYLBEREREZAKDJSIiIiITGCwRERERmcBgiYiIiMgEBktEREREJjBYIiIiIjKBwRIRERGRCQyWiIiIiExgBW8rEATdZpZqtdrBPSEiIiJz6d+39e/jNWGwZAU3b94EAISHhzu4J0RERGSpmzdvwt/fv8bbZUJt4RTVSqvV4tq1a/Dz84NMJnN0d6qlVqsRHh6Oy5cvQ6lUOro7NuVK1wq41vXyWp2XK10vr1U8BEHAzZs3ERYWBrm85swkjixZgVwuR9OmTR3dDbMolUpR/oe1BVe6VsC1rpfX6rxc6Xp5reJgakRJjwneRERERCYwWCIiIiIygcGSi/D09MT8+fPh6enp6K7YnCtdK+Ba18trdV6udL28VulhgjcRERGRCRxZIiIiIjKBwRIRERGRCQyWiIiIiExgsERERERkAoMlJ5Gfn48nn3wSSqUSAQEBmDhxIoqKimpsf+HCBchksmq/vv32W0O76m7ftGmTPS7JJEuvFwD69etX5Vqee+45ozaXLl3C4MGD4ePjg+DgYLz66quorKy05aXUytJrzc/PxwsvvIA2bdrA29sbzZo1w4svvojCwkKjdmJ5bZcvX44WLVrAy8sLMTExSEpKMtn+22+/Rdu2beHl5YUOHTrgxx9/NLpdEATMmzcPoaGh8Pb2RlxcHM6ePWvLSzCbJde6atUq9OnTBw0bNkTDhg0RFxdXpf348eOrvIbx8fG2vgyzWHKt69atq3IdXl5eRm2c5XWt7u+QTCbD4MGDDW3E+rr++uuvGDJkCMLCwiCTybB169Za77N//3506dIFnp6eiIyMxLp166q0sfRvgEMI5BTi4+OFTp06Cb///rvw22+/CZGRkcLjjz9eY/vKykohKyvL6GvBggWCr6+vcPPmTUM7AMLatWuN2t26dcsel2SSpdcrCILQt29fYdKkSUbXUlhYaLi9srJSaN++vRAXFyccP35c+PHHH4XAwEBhzpw5tr4ckyy91pMnTwrDhw8Xtm/fLpw7d07Ys2eP0Lp1a2HEiBFG7cTw2m7atEnw8PAQ1qxZI5w+fVqYNGmSEBAQIOTk5FTb/uDBg4JCoRDeffddITU1VXj99dcFd3d34eTJk4Y2ixcvFvz9/YWtW7cKf/zxhzB06FAhIiLC4f9vLb3WJ554Qli+fLlw/PhxIS0tTRg/frzg7+8vXLlyxdBm3LhxQnx8vNFrmJ+fb69LqpGl17p27VpBqVQaXUd2drZRG2d5XfPy8oyu89SpU4JCoRDWrl1raCPW1/XHH38UXnvtNeH7778XAAhbtmwx2f78+fOCj4+PMH36dCE1NVX4+OOPBYVCISQkJBjaWPr8OQqDJSeQmpoqABCOHDliOLZz505BJpMJV69eNfs80dHRwtNPP210zJxfCHur6/X27dtXeOmll2q8/ccffxTkcrnRH+kVK1YISqVSKCsrs0rfLWWt1/abb74RPDw8hIqKCsMxMby2PXr0EJ5//nnDzxqNRggLCxMWLVpUbfuRI0cKgwcPNjoWExMjPPvss4IgCIJWqxVUKpXw3nvvGW4vKCgQPD09ha+++soGV2A+S6/1TpWVlYKfn5+wfv16w7Fx48YJw4YNs3ZX683Sa127dq3g7+9f4/mc+XX9z3/+I/j5+QlFRUWGY2J9XW9nzt+PmTNnCnfffbfRsVGjRgmDBg0y/Fzf589eOA3nBBITExEQEIBu3boZjsXFxUEul+Pw4cNmnSM5ORkpKSmYOHFilduef/55BAYGokePHlizZg0EB5fmqs/1bty4EYGBgWjfvj3mzJmDkpISo/N26NABISEhhmODBg2CWq3G6dOnrX8hZrDGawsAhYWFUCqVcHMz3g7Ska9teXk5kpOTERcXZzgml8sRFxeHxMTEau+TmJho1B7QvUb69pmZmcjOzjZq4+/vj5iYmBrPaQ91udY7lZSUoKKiAo0aNTI6vn//fgQHB6NNmzaYMmUK8vLyrNp3S9X1WouKitC8eXOEh4dj2LBhRr9zzvy6rl69GqNHj0aDBg2Mjovtda2L2n5frfH82Qs30nUC2dnZCA4ONjrm5uaGRo0aITs726xzrF69Gu3atUOvXr2Mjr/55pvo378/fHx88PPPP+Nf//oXioqK8OKLL1qt/5aq6/U+8cQTaN68OcLCwnDixAnMmjUL6enp+P777w3nvT1QAmD42dzn0dqs8dreuHEDCxcuxOTJk42OO/q1vXHjBjQaTbXP+ZkzZ6q9T02vkf650H831cYR6nKtd5o1axbCwsKM3lji4+MxfPhwREREICMjA//+97/xwAMPIDExEQqFwqrXYK66XGubNm2wZs0adOzYEYWFhVi6dCl69eqF06dPo2nTpk77uiYlJeHUqVNYvXq10XExvq51UdPvq1qtxq1bt/DXX3/V+/fCXhgsidjs2bOxZMkSk23S0tLq/Ti3bt3Cl19+iblz51a57fZjnTt3RnFxMd577z2bvKHa+npvDxY6dOiA0NBQDBgwABkZGWjVqlWdz1sX9npt1Wo1Bg8ejKioKLzxxhtGt9nztaX6Wbx4MTZt2oT9+/cbJT6PHj3a8O8OHTqgY8eOaNWqFfbv348BAwY4oqt1Ehsbi9jYWMPPvXr1Qrt27fDZZ59h4cKFDuyZba1evRodOnRAjx49jI47y+vqTBgsidgrr7yC8ePHm2zTsmVLqFQq5ObmGh2vrKxEfn4+VCpVrY+zefNmlJSUYOzYsbW2jYmJwcKFC1FWVmb1vX7sdb16MTExAIBz586hVatWUKlUVVZh5OTkAIBF5zWHPa715s2biI+Ph5+fH7Zs2QJ3d3eT7W352lYnMDAQCoXC8Bzr5eTk1HhtKpXKZHv995ycHISGhhq1iY6OtmLvLVOXa9VbunQpFi9ejN27d6Njx44m27Zs2RKBgYE4d+6cw95U63Oteu7u7ujcuTPOnTsHwDlf1+LiYmzatAlvvvlmrY8jhte1Lmr6fVUqlfD29oZCoaj3/xV7Yc6SiAUFBaFt27Ymvzw8PBAbG4uCggIkJycb7rt3715otVpDQGDK6tWrMXToUAQFBdXaNiUlBQ0bNrTJm6m9rvf2awFg+OMbGxuLkydPGgUnu3btglKpRFRUlHUu8m+2vla1Wo2BAwfCw8MD27dvr7IMuzq2fG2r4+Hhga5du2LPnj2GY1qtFnv27DEaZbhdbGysUXtA9xrp20dEREClUhm1UavVOHz4cI3ntIe6XCsAvPvuu1i4cCESEhKM8tZqcuXKFeTl5RkFFPZW12u9nUajwcmTJw3X4WyvK6ArgVFWVoYxY8bU+jhieF3rorbfV2v8X7EbR2eYk3XEx8cLnTt3Fg4fPiwcOHBAaN26tdHy8itXrght2rQRDh8+bHS/s2fPCjKZTNi5c2eVc27fvl1YtWqVcPLkSeHs2bPCp59+Kvj4+Ajz5s2z+fXUxtLrPXfunPDmm28KR48eFTIzM4Vt27YJLVu2FO69917DffSlAwYOHCikpKQICQkJQlBQkChKB1hyrYWFhUJMTIzQoUMH4dy5c0bLjysrKwVBEM9ru2nTJsHT01NYt26dkJqaKkyePFkICAgwrEh86qmnhNmzZxvaHzx4UHBzcxOWLl0qpKWlCfPnz6+2dEBAQICwbds24cSJE8KwYcNEs8TckmtdvHix4OHhIWzevNnoNdSX9rh586YwY8YMITExUcjMzBR2794tdOnSRWjdurVQWlrqkGvUs/RaFyxYIPz0009CRkaGkJycLIwePVrw8vISTp8+bWjjLK+rXu/evYVRo0ZVOS7m1/XmzZvC8ePHhePHjwsAhA8++EA4fvy4cPHiRUEQBGH27NnCU089ZWivLx3w6quvCmlpacLy5curLR1g6vkTCwZLTiIvL094/PHHBV9fX0GpVAoTJkwwqpeUmZkpABD27dtndL85c+YI4eHhgkajqXLOnTt3CtHR0YKvr6/QoEEDoVOnTsLKlSurbWtvll7vpUuXhHvvvVdo1KiR4OnpKURGRgqvvvqqUZ0lQRCECxcuCA888IDg7e0tBAYGCq+88orRcntHsPRa9+3bJwCo9iszM1MQBHG9th9//LHQrFkzwcPDQ+jRo4fw+++/G27r27evMG7cOKP233zzjXDXXXcJHh4ewt133y3873//M7pdq9UKc+fOFUJCQgRPT09hwIABQnp6uj0upVaWXGvz5s2rfQ3nz58vCIIglJSUCAMHDhSCgoIEd3d3oXnz5sKkSZNE8yZjybVOmzbN0DYkJER48MEHhWPHjhmdz1leV0EQhDNnzggAhJ9//rnKucT8utb0t0V/fePGjRP69u1b5T7R0dGCh4eH0LJlS6N6Unqmnj+xkAmCg9eBExEREYkYc5aIiIiITGCwRERERGQCgyUiIiIiExgsEREREZnAYImIiIjIBAZLRERERCYwWCIiIiIygcESERERkQkMlojIqfXr1w/Tpk2z2vnGjx+Phx9+uMrxt99+G7169YKPjw8CAgKs9nhE5HgMloiIrKC8vByPPfYYpkyZ4uiuEJGVMVgiIqc1fvx4/PLLL/jwww8hk8kgk8lw4cIFnDp1Cg888AB8fX0REhKCp556Cjdu3DDcb/PmzejQoQO8vb3RuHFjxMXFobi4GG+88QbWr1+Pbdu2Gc63f/9+AMCCBQvw8ssvo0OHDg66WiKyFQZLROS0PvzwQ8TGxmLSpEnIyspCVlYW/Pz80L9/f3Tu3BlHjx5FQkICcnJyMHLkSABAVlYWHn/8cTz99NNIS0vD/v37MXz4cAiCgBkzZmDkyJGIj483nK9Xr14OvkoisjU3R3eAiMhW/P394eHhAR8fH6hUKgDAW2+9hc6dO+Odd94xtFuzZg3Cw8Px559/oqioCJWVlRg+fDiaN28OAEajRd7e3igrKzOcj4icH4MlInIpf/zxB/bt2wdfX98qt2VkZGDgwIEYMGAAOnTogEGDBmHgwIF49NFH0bBhQwf0lojEgNNwRORSioqKMGTIEKSkpBh9nT17Fvfeey8UCgV27dqFnTt3IioqCh9//DHatGmDzMxMR3ediByEwRIROTUPDw9oNBrDz126dMHp06fRokULREZGGn01aNAAACCTyXDPPfdgwYIFOH78ODw8PLBly5Zqz0dEzo/BEhE5tRYtWuDw4cO4cOECbty4geeffx75+fl4/PHHceTIEWRkZOCnn37ChAkToNFocPjwYbzzzjs4evQoLl26hO+//x7Xr19Hu3btDOc7ceIE0tPTcePGDVRUVAAALl26hJSUFFy6dAkajcYwYlVUVOTIyyciK2CwRERObcaMGVAoFIiKikJQUBDKy8tx8OBBaDQaDBw4EB06dMC0adMQEBAAuVwOpVKJX3/9FQ8++CDuuusuvP7663j//ffxwAMPAAAmTZqENm3aoFu3bggKCsLBgwcBAPPmzUPnzp0xf/58FBUVoXPnzoYVd0QkbTJBEARHd4KIiIhIrDiyRERERGQCgyUiIiIiExgsEREREZnAYImIiIjIBAZLRERERCYwWCIiIiIygcESERERkQkMloiIiIhMYLBEREREZAKDJSIiIiITGCwRERERmcBgiYiIiMiE/weYeHVNvNDtFAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1_new = X1.sort_values()\n",
    "theta0 = LR2.intercept_\n",
    "theta1,theta2,theta3,theta4,theta5 = LR2.coef_[0][0],LR2.coef_[0][1],LR2.coef_[0][2],LR2.coef_[0][3],LR2.coef_[0][4]\n",
    "a = theta4\n",
    "b = theta5*X1_new+theta2\n",
    "c = theta0+theta1*X1_new+theta3*X1_new*X1_new\n",
    "X2_new_boundary = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "\n",
    "fig2 = plt.figure()\n",
    "passed=plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed=plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.plot(X1_new,X2_new_boundary)\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112   -0.83007\n",
       "86    -0.75518\n",
       "84    -0.74366\n",
       "111   -0.72638\n",
       "113   -0.72062\n",
       "        ...   \n",
       "70     0.89804\n",
       "65     0.92684\n",
       "68     0.93836\n",
       "67     0.96141\n",
       "101    1.07090\n",
       "Name: test1, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array(b*b-4*a*c)\n",
    "#d = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "X1_new\n",
    "#print(np.array(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define f(x)\n",
    "def f(x):\n",
    "    a = theta4\n",
    "    b = theta5*x+theta2\n",
    "    c = theta0+theta1*x+theta3*x*x\n",
    "    X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    return X2_new_boundary1,X2_new_boundary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.1212617]), array([0.04679448]), array([0.02697935]), array([0.00872189]), array([-0.00830576]), array([-0.00830576]), array([-0.11718731]), array([-0.16040224]), array([-0.18016521]), array([-0.18965258]), array([-0.21671004]), array([-0.22530078]), array([-0.23369452]), array([-0.2499261]), array([-0.28761583]), array([-0.30846849]), array([-0.32171919]), array([-0.32816104]), array([-0.33448523]), array([-0.34069505]), array([-0.35278365]), array([-0.35866807]), array([-0.37013014]), array([-0.42186137]), array([-0.42656594]), array([-0.43119936]), array([-0.43574686]), array([-0.44021779]), array([-0.45318277]), array([-0.47336128]), array([-0.47336128]), array([-0.48465118]), array([-0.48828317]), array([-0.49185073]), array([-0.49185073]), array([-0.51193977]), array([-0.51193977]), array([-0.5181489]), array([-0.52412149]), array([-0.52986615]), array([-0.52986615]), array([-0.53264971]), array([-0.54066023]), array([-0.54572074]), array([-0.55055962]), array([-0.55055962]), array([-0.55518131]), array([-0.56170859]), array([-0.56775529]), array([-0.56775529]), array([-0.58002265]), array([-0.58002265]), array([-0.58588805]), array([-0.59313744]), array([-0.59416537]), array([-0.59514154]), array([-0.59852963]), array([-0.60052615]), array([-0.60280757]), array([-0.60310565]), array([-0.60354216]), array([-0.60379456]), array([-0.60355793]), array([-0.60282483]), array([-0.60105947]), array([-0.60105947]), array([-0.60047489]), array([-0.59135016]), array([-0.59135016]), array([-0.59009769]), array([-0.5887809]), array([-0.58285568]), array([-0.58285568]), array([-0.57390312]), array([-0.56073894]), array([-0.55572236]), array([-0.53219481]), array([-0.52181028]), array([-0.51814159]), array([-0.51814159]), array([-0.50647589]), array([-0.48925514]), array([-0.47987024]), array([-0.46991909]), array([-0.45383618]), array([-0.42349291]), array([-0.40276684]), array([-0.38769565]), array([-0.3714656]), array([-0.353902]), array([-0.34523055]), array([-0.33477608]), array([-0.33477608]), array([-0.32451646]), array([-0.26416907]), array([-0.26416907]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])] [array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.41115349]), array([0.47870382]), array([0.49620931]), array([0.51216114]), array([0.52688317]), array([0.52688317]), array([0.61731967]), array([0.65131207]), array([0.66646378]), array([0.67364552]), array([0.69378609]), array([0.7000712]), array([0.70615931]), array([0.71777963]), array([0.74393719]), array([0.75787296]), array([0.76651241]), array([0.77064862]), array([0.77466719]), array([0.77857137]), array([0.78604871]), array([0.7896275]), array([0.79647831]), array([0.82515323]), array([0.82755217]), array([0.82987596]), array([0.83211783]), array([0.83428312]), array([0.84033121]), array([0.84898157]), array([0.84898157]), array([0.85335458]), array([0.85468094]), array([0.85594286]), array([0.85594286]), array([0.86219812]), array([0.86219812]), array([0.86379599]), array([0.86515732]), array([0.86628671]), array([0.86628671]), array([0.86676464]), array([0.86785827]), array([0.86830752]), array([0.86853594]), array([0.86853594]), array([0.86854596]), array([0.86815595]), array([0.86728536]), array([0.86728536]), array([0.86341226]), array([0.86341226]), array([0.86005458]), array([0.85346939]), array([0.85219128]), array([0.85086182]), array([0.84502699]), array([0.84010621]), array([0.83085948]), array([0.82885193]), array([0.82467718]), array([0.81801268]), array([0.81085516]), array([0.80320517]), array([0.79221728]), array([0.79221728]), array([0.78932708]), array([0.75714603]), array([0.75714603]), array([0.75358794]), array([0.74996551]), array([0.73481778]), array([0.73481778]), array([0.71433305]), array([0.68733509]), array([0.67770724]), array([0.63573465]), array([0.61843322]), array([0.61245891]), array([0.61245891]), array([0.59387632]), array([0.56742904]), array([0.55343288]), array([0.53887047]), array([0.51587067]), array([0.47399923]), array([0.44635628]), array([0.42667382]), array([0.40583252]), array([0.38365766]), array([0.37284469]), array([0.35992047]), array([0.35992047]), array([0.34735121]), array([0.27547567]), array([0.27547567]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\2\\ipykernel_8568\\1482556590.py:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\2\\ipykernel_8568\\1482556590.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    }
   ],
   "source": [
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for x in X1_new:\n",
    "    X2_new_boundary1.append(f(x)[0])\n",
    "    X2_new_boundary2.append(f(x)[1])\n",
    "print(X2_new_boundary1,X2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure()\n",
    "passed=plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed=plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.plot(X1_new,X2_new_boundary1)\n",
    "plt.plot(X1_new,X2_new_boundary2)\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\2\\ipykernel_8568\\1482556590.py:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\2\\ipykernel_8568\\1482556590.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  X2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    }
   ],
   "source": [
    "X1_range = [-0.9 + x/10000 for x in range(0,19000)]\n",
    "X1_range = np.array(X1_range)\n",
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for x in X1_range:\n",
    "    X2_new_boundary1.append(f(x)[0])\n",
    "    X2_new_boundary2.append(f(x)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "import matplotlib as mlp\n",
    "mlp.rcParams['font.family'] = 'SimHei'\n",
    "mlp.rcParams['axes.unicode_minus'] = False\n",
    "fig4 = plt.figure()\n",
    "passed=plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed=plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.plot(X1_range,X2_new_boundary1,'r')\n",
    "plt.plot(X1_range,X2_new_boundary2,'r')\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('测试1')\n",
    "plt.ylabel('测试2')\n",
    "plt.title('芯片质量预测')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
